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COBASE: A new copula-based shuffling method for ensemble weather forecast postprocessing
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DOI：10.1002/qj.70138.png)
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Abstract
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Weather predictions are often provided as ensembles generated by repeated runs of numerical weather prediction models. These forecasts typically exhibit bias and inaccurate dependence structures due to numerical and dispersion errors, requiring statistical postprocessing for improved precision. A common correction strategy is the two-step approach: first adjusting the univariate forecasts, then reconstructing the multivariate dependence. The second step is usually handled with non-parametric methods, which can underperform when historical data are limited. Parametric alternatives, such as the Gaussian copula approach (GCA), offer theoretical advantages but often produce poorly calibrated multivariate forecasts, due to random sampling of the corrected univariate margins. In this work, we introduce Copula-Based Shuffling of Ensemble (COBASE), a novel copula-based postprocessing framework that preserves the flexibility of parametric modeling while mimicking the non-parametric techniques through a rank-shuffling mechanism. This design ensures calibrated margins and realistic dependence reconstruction. We evaluate COBASE on multi-site 2-m temperature forecasts from the Aire Limit & eacute;e Adaptation dynamique D & eacute;veloppement InterNational-Limited Area Ensemble Forecasting (ALADIN-LAEF) ensemble over Austria and on joint forecasts of temperature and dew-point temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) system in the Netherlands. Across all regions, COBASE variants consistently outperform traditional copula-based approaches, such as GCA, and achieve a performance on par with state-of-the-art non-parametric methods like SimSchaake and ensemble copula coupling (ECC), with only minimal differences across settings. These results position COBASE as a competitive and robust alternative for multivariate ensemble postprocessing, offering a principled bridge between parametric and non-parametric dependence reconstruction.
Keywords:
ALADIN-LAEF ensemble system
dependence modeling and copulas
ECMWF
statistical postprocessing of weather forecasts
two-step parametric correction
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